Beating the competition with Cognitive Commerce

Artificial Intelligence, and particularly machine learning, is the talk of the tech industry and how it’s going to revolutionize every sector. So what is it? What do you need to know? And how can you use it as a tool to get an edge? This session will look at some of the recent developments and use cases, and how they can be used to differentiate and excel in eCommerce. Topics will range from intelligent search, to customer service channels of Facebook Messenger and Amazon Alexa, and to finding swimming pools in photos with IBM Watson.

Transcript

2. Beating the Competition with Cognitive Commerce
3. Tom Robertshaw Founder & CEO of Meanbee @bobbyshaw
4. Meanbee • UK eCommerce Agency • Specialized in Magento • Technology First • Client revenues average $2-10 million
5. Cognitive what?
6. Artificial Intelligence • The idea that human thought can be replicated
7. Machine Learning • Subset of AI • Ability to spot correlations between properties and effects • Supervised • Unsupervised • Reinforcement
8. Supervised Learning • This is the one we’re quite good • Provide example inputs and responses • Learn correlations between them
9. Supervised Learning Input Response Emails Spam Image Object Audio Text English French
10. Why now? Computers are fast enough to train on large datasets in a reasonable time with the same or better accuracy than a person
11. Cognitive Commerce • Facilitating commerce growth using machine learning with: • New interaction channels • More automation
12. Cognitive Commerce • It’s about being smarter: • Being smarter in how we interact with customers • Being smarter in how we manage our store • Being smarter in how we spend our time
13. Competitive Advantage • Increase budget • Work more hours • Work more efficiently
14. Working Efficiently • Productivity • Working on the right tasks • Maintaining a good cost/benefit ratio
15. Automation • Tasks that we don’t need to be doing • Tasks that weren’t previously feasible
16. What can be automated? • A good heuristic from Andrew Ng: “” Anything that a human can do with less than one second of thought
17. Automate or be automated • Estimates suggest between 9% to 47% of jobs can be automated • Not just factory workers • Japanese Company replaced insurance claim agents with IBM Watson
18. You already use machine learning
19. Personalization • Machine Learning under our noses • Getting cheaper and easier • Product personalization popular, e.g. Nosto • Content personalization remains a challenge
20. Fraud Detection • Used machine learning models for years • Accuracy will continue to increase • Become tailored to your business
21. Shiny shiny hype
22. Voice Assistants • Google Assistant • Amazon Alexa • Microsoft Cortana • Apple Siri • Samsung Bixby
23. Conversational Commerce • New channels for Customer Service & Pre-Sales • Being available to customers at the right time
24. Amazon Alexa • Estimated 5.2m sold last year • Customer spending increased 10% after purchase • Immersed in home life
25. Amazon Alexa
26. Natural Language Search • “Red waterproof jacket under $200” • Going beyond keywords to pull out and filter intent • Klevu are one of the leading examples of ML in search
28. Chat Bots • Facebook Messenger • Order Confirmation and Updates • Automate answers to basic support requests
29. Advanced Segmentation • Segmenting based on product history is nice • Segmenting customers based on their personality and desires • Tailor the messaging to them
30. Intelligent Remarketing • We segment to avoid blasting all customers • Most ML budget is focused on predicting ad clicks • Reduce remarketing wastage
31. Predictive Infrastructure Scaling • Scale before load is an issue • Learn from tell-tale signs • Spin up further resources so that they’ll be ready in time
32. Anticipatory shipping • Patented by Amazon • Shipping to a general area based on predicted orders • Put the final address on package during transit
33. Anomaly Detection • Static measurements of traffic or actions aren’t useful • Learn typical site usage taking into account • Seasonality • Time of day • Marketing campaigns
34. Catalog Management • Detect Inconsistencies • “Shouldn’t this product be in the Handbags category?” • “Cameras normally have a 360 zoom image, should this 
 product have one?”
35. Catalog Management • Imagine only having to categorize products once • New products automatically assigned to categories • Perhaps pre-fill attributes based on description
36. ML for your business today
37. What • Step back and profile your teams daily tasks • Experiment with time tracking to review where time goes • Discuss processes • Identify repetitive tasks • Include a technical representative if possible
38. What • Busyness is dangerous to your business • It makes us feel productive and satisfied • Where is the value really coming from? • What are we wasting time on?
39. How • Depends on your technical resources • If you have some, look into APIs, e.g. IBM Watson
40. IBM Watson • Natural language Analysis • Solr Search that can be trained • Personality Insights • Tone Analyser • Trade-off analytics • Image recognition
43. Kyero • 100,000 properties • Initial tests show 80% accuracy identifying primary photo
44. How • Supplier selection process • Choose tools that have incorporated ML • ML doesn’t make a product • But all great products will be using ML
45. $2,000,000,000,000 market